Add strategy to store results in evaluation loop

src/transformers/trainer.py

@@ -104,6 +104,7 @@
 from .trainer_utils import (
     PREFIX_CHECKPOINT_DIR,
     BestRun,
+    EvalLoopContainer,
     EvalLoopOutput,
     EvalPrediction,
     HPSearchBackend,
@@ -3627,20 +3628,14 @@ def evaluation_loop(
             self._past = None
 
         # Initialize containers
-        # losses/preds/labels on GPU/TPU (accumulated for eval_accumulation_steps)
-        losses_host = None
-        preds_host = None
-        labels_host = None
-        inputs_host = None
-
-        # losses/preds/labels on CPU (final containers)
-        all_losses = None
-        all_preds = None
-        all_labels = None
-        all_inputs = None
-        # Will be useful when we have an iterable dataset so don't know its length.
+        all_losses = EvalLoopContainer(self.args.eval_do_concat_batches, padding_index=-100)
+        all_preds = EvalLoopContainer(self.args.eval_do_concat_batches, padding_index=-100)
+        all_labels = EvalLoopContainer(self.args.eval_do_concat_batches, padding_index=-100)
+        all_inputs = EvalLoopContainer(self.args.eval_do_concat_batches, padding_index=-100)
 
+        # Will be useful when we have an iterable dataset so don't know its length.
         observed_num_examples = 0
+
         # Main evaluation loop
         for step, inputs in enumerate(dataloader):
             # Update the observed num examples
@@ -3659,56 +3654,33 @@ def evaluation_loop(
             if is_torch_xla_available():
                 xm.mark_step()
 
-            # Update containers on host
+            # Update containers
             if loss is not None:
                 losses = self.gather_function((loss.repeat(batch_size)))
-                losses_host = losses if losses_host is None else nested_concat(losses_host, losses, padding_index=-100)
-            if labels is not None:
-                labels = self.accelerator.pad_across_processes(labels, dim=1, pad_index=-100)
+                all_losses.add(losses)
             if inputs_decode is not None:
                 inputs_decode = self.accelerator.pad_across_processes(inputs_decode, dim=1, pad_index=-100)
                 inputs_decode = self.gather_function((inputs_decode))
-                inputs_host = (
-                    inputs_decode
-                    if inputs_host is None
-                    else nested_concat(inputs_host, inputs_decode, padding_index=-100)
-                )
+                all_inputs.add(inputs_decode)
             if logits is not None:
                 logits = self.accelerator.pad_across_processes(logits, dim=1, pad_index=-100)
                 if self.preprocess_logits_for_metrics is not None:
                     logits = self.preprocess_logits_for_metrics(logits, labels)
                 logits = self.gather_function((logits))
-                preds_host = logits if preds_host is None else nested_concat(preds_host, logits, padding_index=-100)
-
+                all_preds.add(logits)
             if labels is not None:
+                labels = self.accelerator.pad_across_processes(labels, dim=1, pad_index=-100)
                 labels = self.gather_function((labels))
-                labels_host = labels if labels_host is None else nested_concat(labels_host, labels, padding_index=-100)
+                all_labels.add(labels)
 
             self.control = self.callback_handler.on_prediction_step(args, self.state, self.control)
 
             # Gather all tensors and put them back on the CPU if we have done enough accumulation steps.
             if args.eval_accumulation_steps is not None and (step + 1) % args.eval_accumulation_steps == 0:
-                if losses_host is not None:
-                    losses = nested_numpify(losses_host)
-                    all_losses = losses if all_losses is None else np.concatenate((all_losses, losses), axis=0)
-                if preds_host is not None:
-                    logits = nested_numpify(preds_host)
-                    all_preds = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
-                if inputs_host is not None:
-                    inputs_decode = nested_numpify(inputs_host)
-                    all_inputs = (
-                        inputs_decode
-                        if all_inputs is None
-                        else nested_concat(all_inputs, inputs_decode, padding_index=-100)
-                    )
-                if labels_host is not None:
-                    labels = nested_numpify(labels_host)
-                    all_labels = (
-                        labels if all_labels is None else nested_concat(all_labels, labels, padding_index=-100)
-                    )
-
-                # Set back to None to begin a new accumulation
-                losses_host, preds_host, inputs_host, labels_host = None, None, None, None
+                all_losses.to_cpu_and_numpy()
+                all_preds.to_cpu_and_numpy()
+                all_labels.to_cpu_and_numpy()
+                all_inputs.to_cpu_and_numpy()
 
         # After all calls to `.gather_function`, reset to `gather_for_metrics`:
         self.gather_function = self.accelerator.gather_for_metrics
@@ -3717,20 +3689,10 @@ def evaluation_loop(
             delattr(self, "_past")
 
         # Gather all remaining tensors and put them back on the CPU
-        if losses_host is not None:
-            losses = nested_numpify(losses_host)
-            all_losses = losses if all_losses is None else np.concatenate((all_losses, losses), axis=0)
-        if preds_host is not None:
-            logits = nested_numpify(preds_host)
-            all_preds = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)
-        if inputs_host is not None:
-            inputs_decode = nested_numpify(inputs_host)
-            all_inputs = (
-                inputs_decode if all_inputs is None else nested_concat(all_inputs, inputs_decode, padding_index=-100)
-            )
-        if labels_host is not None:
-            labels = nested_numpify(labels_host)
-            all_labels = labels if all_labels is None else nested_concat(all_labels, labels, padding_index=-100)
+        all_losses = all_losses.get_arrays()
+        all_preds = all_preds.get_arrays()
+        all_labels = all_labels.get_arrays()
+        all_inputs = all_inputs.get_arrays()
 
         # Number of samples
         if has_length(eval_dataset):
@@ -3761,7 +3723,9 @@ def evaluation_loop(
         # To be JSON-serializable, we need to remove numpy types or zero-d tensors
         metrics = denumpify_detensorize(metrics)
 
-        if all_losses is not None:
+        if isinstance(all_losses, list) and all_losses:
+            metrics[f"{metric_key_prefix}_loss"] = np.concatenate(all_losses).mean().item()
+        elif isinstance(all_losses, np.ndarray):
             metrics[f"{metric_key_prefix}_loss"] = all_losses.mean().item()
         if hasattr(self, "jit_compilation_time"):
             metrics[f"{metric_key_prefix}_jit_compilation_time"] = self.jit_compilation_time
@@ -4204,6 +4168,7 @@ def prediction_loop(
         logger.info(f"***** Running {description} *****")
         logger.info(f"  Num examples = {num_examples}")
         logger.info(f"  Batch size = {batch_size}")
+
         losses_host: torch.Tensor = None
         preds_host: Union[torch.Tensor, List[torch.Tensor]] = None
         labels_host: Union[torch.Tensor, List[torch.Tensor]] = None

src/transformers/trainer_utils.py

@@ -29,6 +29,7 @@
 
 import numpy as np
 
+from .trainer_pt_utils import nested_concat, nested_numpify
 from .utils import (
     ExplicitEnum,
     is_psutil_available,
@@ -199,6 +200,58 @@ class TrainOutput(NamedTuple):
     metrics: Dict[str, float]
 
 
+class EvalLoopContainer:
+    """
+    Container to store intermediate results of evaluation loop
+
+    Args:
+        do_nested_concat (`bool`, *optional*, defaults to `True`):
+            If set to `True`, each iteration will recursively concatenate a new object containing tensors to
+            the existing stored tensors, provided that the structure of the existing object and the new one
+            are identical. If set to `False`, all newly added tensors will be stored in a list.
+        padding_index (`int`, *optional*, defaults to -100):
+            Value used to pad tensors of different shapes when `do_nested_concat=True`.
+    """
+
+    def __init__(self, do_nested_concat: bool = True, padding_index: int = -100):
+        self.do_nested_concat = do_nested_concat
+        self.padding_index = padding_index
+        self.tensors = None
+        self.arrays = None
+
+    def add(self, tensors) -> None:
+        """Add tensors to the stored objects. If `do_nested_concat=True`, the tensors will be concatenated recursively."""
+        if self.tensors is None:
+            self.tensors = tensors if self.do_nested_concat else [tensors]
+        elif self.do_nested_concat:
+            self.tensors = nested_concat(self.tensors, tensors, padding_index=self.padding_index)
+        else:
+            self.tensors.append(tensors)
+
+    def to_cpu_and_numpy(self) -> None:
+        """Move tensors in stored objects to CPU and convert them to numpy arrays."""
+
+        # Check if we have something to add, if not just return
+        if self.tensors is None:
+            return
+
+        new_arrays = nested_numpify(self.tensors)
+        if self.arrays is None:
+            self.arrays = new_arrays
+        elif self.do_nested_concat:
+            self.arrays = nested_concat(self.arrays, new_arrays, padding_index=self.padding_index)
+        else:
+            self.arrays.extend(new_arrays)
+
+        # reset device tensors after adding to cpu
+        self.tensors = None
+
+    def get_arrays(self):
+        """Returns the numpified and moved to CPU stored objects."""
+        self.to_cpu_and_numpy()
+        return self.arrays
+
+
 PREFIX_CHECKPOINT_DIR = "checkpoint"
 _re_checkpoint = re.compile(r"^" + PREFIX_CHECKPOINT_DIR + r"\-(\d+)$")
 

src/transformers/training_args.py

@@ -639,6 +639,9 @@ class TrainingArguments:
         include_inputs_for_metrics (`bool`, *optional*, defaults to `False`):
             Whether or not the inputs will be passed to the `compute_metrics` function. This is intended for metrics
             that need inputs, predictions and references for scoring calculation in Metric class.
+        eval_do_concat_batches (`bool`, *optional*, defaults to `True`):
+            If set to `False`, inputs/losses/labels/predictions are stored as lists, with each batch kept separate.
+            If set to `True`, tensors in these nested objects are recursively concatenated across batches.
         auto_find_batch_size (`bool`, *optional*, defaults to `False`)
             Whether to find a batch size that will fit into memory automatically through exponential decay, avoiding
             CUDA Out-of-Memory errors. Requires accelerate to be installed (`pip install accelerate`)
@@ -1261,6 +1264,12 @@ class TrainingArguments:
     include_inputs_for_metrics: bool = field(
         default=False, metadata={"help": "Whether or not the inputs will be passed to the `compute_metrics` function."}
     )
+    eval_do_concat_batches: bool = field(
+        default=True,
+        metadata={
+            "help": "Whether or not tensors in nested objects in batches should be recursively concatenated between batches."
+        },
+    )
     # Deprecated arguments
     fp16_backend: str = field(
         default="auto",

tests/trainer/test_trainer_utils.py

@@ -20,7 +20,7 @@
 
 from transformers.data.data_collator import default_data_collator
 from transformers.testing_utils import require_accelerate, require_torch
-from transformers.trainer_utils import RemoveColumnsCollator, find_executable_batch_size
+from transformers.trainer_utils import EvalLoopContainer, RemoveColumnsCollator, find_executable_batch_size
 from transformers.utils import is_torch_available
 
 
@@ -497,3 +497,92 @@ def info(self, msg):
         remove_columns_collator(data_batch)
         self.assertEqual(logger.called, 1)
         self.assertIn("col3", logger.last_msg)
+
+    def test_eval_loop_container(self):
+        batch_1 = [
+            torch.ones([8, 5]),
+            {"loss": torch.tensor(1.0)},
+            (torch.ones([8, 2, 3]), torch.ones([8, 2])),
+        ]
+        batch_2 = [
+            torch.ones([4, 5]),
+            {"loss": torch.tensor(2.0)},
+            (torch.ones([4, 2, 3]), torch.ones([4, 6])),
+        ]
+
+        concat_container = EvalLoopContainer(do_nested_concat=True, padding_index=-100)
+        concat_container.add(batch_1)
+        concat_container.add(batch_2)
+        concat_container.to_cpu_and_numpy()
+        arrays = concat_container.get_arrays()
+
+        # Test two nested batches concatenation
+        self.assertIsInstance(arrays, list)
+        self.assertEqual(len(arrays), 3)
+        self.assertIsInstance(arrays[0], np.ndarray)
+        self.assertEqual(arrays[0].shape, (12, 5))
+        self.assertIsInstance(arrays[1], dict)
+        self.assertIsInstance(arrays[1]["loss"], np.ndarray)
+        self.assertEqual(arrays[1]["loss"].shape, (2,))
+        self.assertTrue(np.allclose(arrays[1]["loss"], np.array([1.0, 2.0])))
+        self.assertIsInstance(arrays[2], tuple)
+        self.assertEqual(len(arrays[2]), 2)
+        self.assertEqual(arrays[2][0].shape, (12, 2, 3))
+        self.assertEqual(arrays[2][1].shape, (12, 6))
+        # check that first batch padded with padding index -100 after concatenation
+        self.assertEqual(arrays[2][1][0][2], -100)
+
+        # Test two batches with no concatenation
+        list_container = EvalLoopContainer(do_nested_concat=False)
+        list_container.add(batch_1)
+        list_container.add(batch_2)
+        list_container.to_cpu_and_numpy()
+        arrays = list_container.get_arrays()
+
+        self.assertEqual(len(arrays), 2)
+        self.assertIsInstance(arrays, list)
+        np_batch_1, np_batch_2 = arrays
+
+        self.assertIsInstance(np_batch_1, list)
+        self.assertEqual(len(np_batch_1), 3)
+        self.assertIsInstance(np_batch_1[0], np.ndarray)
+        self.assertIsInstance(np_batch_1[1], dict)
+        self.assertIsInstance(np_batch_1[2], tuple)
+        self.assertEqual(np_batch_1[0].shape, (8, 5))
+        self.assertEqual(np_batch_1[1]["loss"].shape, ())
+        self.assertEqual(np_batch_1[2][0].shape, (8, 2, 3))
+        self.assertEqual(np_batch_1[2][1].shape, (8, 2))
+
+        self.assertIsInstance(np_batch_2, list)
+        self.assertEqual(len(np_batch_2), 3)
+        self.assertIsInstance(np_batch_2[0], np.ndarray)
+        self.assertIsInstance(np_batch_2[1], dict)
+        self.assertIsInstance(np_batch_2[2], tuple)
+        self.assertEqual(np_batch_2[0].shape, (4, 5))
+        self.assertEqual(np_batch_2[1]["loss"].shape, ())
+        self.assertEqual(np_batch_2[2][0].shape, (4, 2, 3))
+        self.assertEqual(np_batch_2[2][1].shape, (4, 6))
+
+        # Test no batches
+        none_arr = EvalLoopContainer(do_nested_concat=True, padding_index=-100).get_arrays()
+        self.assertIsNone(none_arr)
+
+        none_arr = EvalLoopContainer(do_nested_concat=False).get_arrays()
+        self.assertIsNone(none_arr)
+
+        # Test one batch
+        concat_container = EvalLoopContainer(do_nested_concat=True, padding_index=-100)
+        concat_container.add(batch_1)
+        arrays = concat_container.get_arrays()
+        self.assertIsInstance(arrays, list)
+        self.assertEqual(len(arrays), 3)
+        self.assertIsInstance(arrays[0], np.ndarray)
+        self.assertEqual(arrays[0].shape, (8, 5))
+        self.assertIsInstance(arrays[1], dict)
+        self.assertIsInstance(arrays[1]["loss"], np.ndarray)
+        self.assertEqual(arrays[1]["loss"].shape, ())
+        self.assertTrue(np.allclose(arrays[1]["loss"], np.array([1.0])))
+        self.assertIsInstance(arrays[2], tuple)
+        self.assertEqual(len(arrays[2]), 2)
+        self.assertEqual(arrays[2][0].shape, (8, 2, 3))
+        self.assertEqual(arrays[2][1].shape, (8, 2))